Application of optimal stopping to model sales in financial markets: Examination and analysis

Document Type : Research Paper

Authors

Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

High-precision prediction of financial prices is still a deemed long-term challenge that constantly calls for state-of-the-art approaches. Thus, the purpose of the current study was to examine the efficacy of optimal stopping and use its connection with branching processes to predict several financial markets. For this purpose, the S&P 500 index and dollar, gold, oil and bitcoin markets are predicted at 5-, 10-, 30-, 50-, and 100-day forecast horizons, for each of which the optimal buying and selling point was determined. Closing price data for at least 2200 trading days during the period 2013-2021 was used for the purposes of this study. Moreover, given that any prediction and decision in the financial markets is highly based on probability, and hence risk, two strategies are devised for examination, namely (1) high risk (success rate of at least 50%) and (2) low risk (success rate of at least 70%). The findings indicated that the estimations on all the indices and prices were relevant for the high-risk scenario (that is a success rate of at least 50%), while only those on the S&P 500 index and price of gold were relevant for the low-risk scenario (success rate of at least 70%).

Keywords

[1] H. Alidoost, M. . Abbaszadeh and M. Jabbari Nooghabi, Measuring the impact of the (2011-2012) financial crisis on the relationship between financial ratios and bank profits, Trans. Data Anal. Soc. Sci. 1 (2019), no. 1, 33–42.
[2] D. Assaf, L. Goldstein and E. Samuel-Chan, An unexpected connection between branching processes and optimal stopping, J Appl Probab. 37 (2000), 613–626.
[3] Y. Chow, H. Robbin and D. Siegmund, Great Expectations: The Theory of Optimal Stopping, Houghton, Mifflin, Boston, 1971.
[4] S. Dolatkhah Takloo and M. Mardani, Mechanically closed loop gearbox test rig controller, Trans. Machine Intel. 3 (2020), no. 1, 1–13.
[5] C. Dragomirescu-Gaina, D. Philippas and M. Tsionas, Trading off accuracy for speed: Hedge funds’ decisionmaking under uncertainty, Int. Rev. Finan. Anal. 75 (2021).
[6] A. Fathan and E. Delage, Deep reinforcement learning for optimal stopping with application in financial engineering, arXiv:2105.08877v1 [cs.AI]. (2021).
[7] T. Harris, The Theory of Branching Processes, Springer, Berlin, 1963.
[8] N. Jafari Azarki and M. Noorbakhsh Langrudi, The impact of interest rate changes on stock returns of private banks accepted in Tehran Stock Exchange, Trans. Data Anal. Soc. Sci. 2 (2020), no. 1.
[9] S. Karlin and H. Taylor, A First Course in Stochastic Process, Academic Press, New York, 1975.
[10] M. Kim, A data mining framework for financial prediction, Expert Syst Appl. 173 (2021).
[11] Ch. Liu and J. Wang, Forecasting of energy futures market and synchronization based on stochastic gated recurrent unit model, Energy. 213 (2020).
[12] A. Moud, F. Grabill and D. Boes, Introduction to the Theory of Statistics, Mc GrawHill Inc, 1973.
[13] A.A. Rastegar and Z. Sharei, The relationship between reward management system and employee performance and motivation, Trans. Data Anal. Soc. Sci. 2 (2020), no. 1, 36—44.
[14] Sh. Ross, Stochastic Processes, John Wily & Sons, New York, 1983.
[15] F. Rotondi, Optimal stopping theory and American options, Seminario Dottorato’s, Universita di Padova– Dipartimento di Matematica ‘Tullio Levi-Civita’, 2020.
[16] V. Shah, Optimal Stopping Problems: Autonomous Trading over an Infinite Time horizon (MSc thesis), Imperial College London Department of Mathematics, 2020.
[17] Z. Shishehbor, A. Nematollahi, N. Sanjari and H. Daneshmand, Unexpected connection between branching processes and optimal stopping, MSc Thesis, University of Shiraz, 2004.
[18] D. Wong, Generalised optimal stopping problems and financial markets, Chapman & Hall/CRC Research Notes in Mathematics Series, 2017.
Volume 14, Issue 12
December 2023
Pages 299-304
  • Receive Date: 17 December 2022
  • Revise Date: 24 January 2023
  • Accept Date: 20 February 2023